As shown in Table REF , our STEP framework consistently achieves the best performance in almost all horizons in all datasets, indicating the effectiveness of our framework.
GTS and MTGNN jointly learn the graph structure among multiple time series and the spatial-temporal graph neural networks.
GTS extends DCRNN by introducing a neighborhood graph as a regularization to improve graph quality and reformulates the problem as a unilevel optimization problem.
MTGNN replaces the GNN and Gated TCN in Graph WaveNet with mix-hop propagation layer [1]} and dilated inception layer, and proposes to learn latent adjacency matrix to seek further improvement.
However, they can not consistently outperform other baselines.
Kindly note that the results of GTS may have some gaps with the original paper because it calculates the evaluation metrics in a slightly different manner.
Some details can be found in the appendix in the original paper [2]} and similar issues in its official code repositoryhttps://github.com/chaoshangcs/GTS/issues.
We unify the evaluation process with other baselines, run GTS five times, and report its best performance.
GMAN performs better in long-term prediction benefiting from the powerful ability of the attention mechanism in capturing long-term dependency.
DCRNN and Graph WaveNet are two typical spatial-temporal graph neural networks.
Even compared with many newer works such as ASTGCN and STSGCN, their performance is still very promising.
This may be due to their refined and reasonable model architecture.
FC-LSTM, a classic recurrent neural network, can not perform well since it only considers temporal features, ignoring the dependencies between time series.
Other non-deep learning methods HA, VAR, and SVR perform worst since they have strong assumptions about the data, e.g., stationary or linear.
Thus, they can not capture the strong nonlinear and dynamic spatial and temporal correlations in real-world datasets.
